Big Data and Analytics - Coursework Example

Big Data and Analytics al Affiliation Table of Contents 3 Introduction 4 Big Data Analytics: Overview 4 Major Challenges faced by a Manager in the Implementation of Big Data Analytics 7 Examples on Overcoming Obstacles 8 Conclusion 9 References 10 Abstract The current discourse hereby aims to survey and explore the major challenges a manager faces when considering implementation of big data analytics. The paper would provide an initial overview of big data analytics through the provision of relevant terminologies including big data, analytics, and big data analytics. Likewise, the major challenges faced by managers would be explored and expounded, including examples of overcoming the obstacles that were revealed. From the information gathered and reviewed, practitioners of big data analytics recognize how effective conceptual knowledge of data analytics would assist in overcoming diverse obstacles in varied organizational settings. As such, through experience and skills developed in big data analytics, managers are provided with the most effective tool which would facilitate decision-making in the most viably supported manner. Big Data and Analytics Introduction In the fast pace of globalization, driving forces include technological and communication advancements that magnified access to vast sources of information. The proliferation of immense wealth of information has been observed to exhibit unprecedented growths. As such, for organizations whose main thrust of operations entail the use of big data, it is perceived to be extremely challenging for managers to perform the traditional functions such as planning, organizing, directing, and controlling diverse facets of organizational performance. In this regard, the current discourse hereby aims to survey and explore the major challenges a manager faces when considering implementation of big data analytics. The paper would provide an initial overview of big data analytics through the provision of relevant terminologies including big data, analytics, and big data analytics. Likewise, the major challenges faced by managers would be explored and expounded, including examples of overcoming the obstacles that were revealed. Big Data Analytics: Overview In an article written by Press (2014), the author illumined readers on various definitions of the term, big data. As noted in the definition provided by the Oxford English Dictionary, big data was defined as “data of a very large size, typically to the extent that its manipulation and management present significant logistical challenges” (Press, 2014, p. 1). However, as emphasized by Sharda, Delen, & Turban (2014), the definition and application of the term ‘big data’ is actually relative since the concept of big depends on the size of the organization. As such, the term was described in terms of common traits or features such as: volume, variety, and velocity (Sharda, Delen, & Turban, 2014). Volume was explained, not only in terms of wealth of information; but more so, on the value of the information collected to the organization. Next, variety was described through types of formats (structured and semi-structured; ranging from “traditional databases to hierarchical data stores created by the end users and OLAP Systems. Additional modern data types include video, audio, and stock ticker data” (Sharda, Delen, & Turban: Definition of Big Data, 2014, par. 3). Finally, velocity was defined as “how fast data is being produced and how fast the data must be processed to meet the demand” (Sharda, Delen, & Turban: Definition of Big Data, 2014, par. 3). The decisions that are categorized according to structuredess are presented as Table 1, below: Table 1: Decision Categories by Degree of Problem Structuredness Source: Lubicz, 2014 Concurrently, analytics was described as widely encompassing in terms of evaluating various sources of information depending on the needs and requirements of the organization. More specifically, analytics was defined as follows: “the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen” (Gartner, Inc., 2013, p. 1). Finally, big data analytics is aptly defined as “the process of collecting, organizing and analyzing large sets of data ("big data") to discover patterns and other useful information. Big data analytics will help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Big data analysts basically want the knowledge that comes from analyzing the data” (Beal, 2015, p. 1). Due to the wealth of information that continue to evolve, managing and implementing big data analytics was deemed to be challenging. In addition, as emphasized by Sharda, Delen, & Turban (2014), there were prominent problems associated with big data including process efficiency, as well as cost minimization and customer satisfaction issues. Further,other business problems that were noted to be directly associated and attributed to big data analytics include the following: “brand management, revenue maximization, churn identification, risk management, regulatory compliance and enhanced security capabilities” (Sharda, Delen, & Turban: Fundamentals of Big Data Analytics, 2014, par. 2). From an evaluation of these problems, it could be deduced that all fall under the jurisdication of managerial functions and require the theoretical knowledge and skills expected of contemporary managers. Major Challenges faced by a Manager in the Implementation of Big Data Analytics Since big data analytics focus on the evaluation and processing of vast amounts of information that should be streamlined to cater to the specialized needs of the organization, managers’ roles and responsibilities were discerned to be more challenging. The challenges faced by managers include the following: “The first challenge is in breaking down data silos to access all data an organization stores in different places and often in different systems. A second big data challenge is in creating platforms that can pull in unstructured data as easily as structured data. This massive volume of data is typically so large that its difficult to process using traditional database and software methods” (Beal, 2015, p. 1). From these challenges, managers are expected to be knowledgeable in the creation of platforms that would determine how unstructured data could be converted or transformed to structured data and be more useful to the organization. In addition, managers’ ability to select appropriate software methods to facilitate the use of data is integral in the implementation of big data analytics. As revealed, “to analyze such a large volume of data, big data analytics is typically performed using specialized software tools and applications for predictive analytics, data mining, text mining, forecasting and data optimization. Collectively these processes are separate but highly integrated functions of high-performance analytics. Using big data tools and software enables an organization to process extremely large volumes of data that a business has collected to determine which data is relevant and can be analyzed to drive better business decisions in the future” (Beal, 2015, p. 1). Therefore, aside from enabling the analysis of relevant information, managers are also expected to use the information for responsible and viable decision making that would achieve identified goals. Examples on Overcoming Obstacles The examples of managers’ abilities to overcome the identified challenges were noted through the following applications: “decode human DNA in minutes, predict where terrorists plan to attack, determine which gene is mostly likely to be responsible for certain diseases and, of course, which ads you are most likely to respond to on Facebook” (Beal, 2015, p. 1). In addition, when managers are faced with the need to evaluate a venture option to international markets, big data analytics would provide the needed information on the opportunities, growth prospects, competition, and other external factors that impinge on the planned countries where venturing organizations plan to pursue establishment of branches. Therefore, opportunities are evaluated against threats; as well as strengths against weaknesses. The overall benefits or costs that would be generated would assist in deciding whether to push through with the intended international venture. In an article written by Sullivan & Mitra (2014), the authors applied big data analytics in predicting “which residents are likely to move away, and … help us infer which factors of city life and city services contribute to a resident’s decision to leave the city” (p. 1). The results of their study disclosed that “factors like public transportation services, public schools, and personal finances are significant in this regard, which can potentially help the city of San Francisco to prioritize its resources in order to better retain its locals” (Sullivan & Mitra, 2014, p. 1). From the study and the experience, it could be inferred that the implementation of big data analytics is instrumental in making viable decisions after crucial information has been streamlined and analyzed, according to the needs of the decision-makers. Conclusion The current discourse achieved its goal of surveying and exploring the major challenges that managers face when considering implementation of big data analytics. From the information gathered and reviewed, practitioners of big data analytics recognize how effective conceptual knowledge of data analytics would assist in overcoming diverse obstacles in varied organizational settings. As such, through experience and skills developed in big data analytics, managers are provided with the most effective tool which would facilitate decision-making in the most viably supported manner. References Beal, V. (2015). Big Data Analytics. Retrieved January 25, 2015, from webopedia.com: http://www.webopedia.com/TERM/B/big_data_analytics.html Gartner, Inc. (2013). Analytics. Retrieved January 25, 2015, from IT Glossary: http://www.gartner.com/it-glossary/analytics Lubicz, W. (2014). Decision Support Systems: An Overview of Managerial Decision Support, Business Intelligence and Analytics. Retrieved January 25, 2015, from Wroclaw University of Technology: http://www.ioz.pwr.wroc.pl/Pracownicy/lubicz/DSS/LECTURES/DSS%202014%2001.pdf Press, G. (2014, September 3). 12 Big Data Definitions: Whats Yours? Retrieved January 25, 2015, from Forbes: http://www.forbes.com/sites/gilpress/2014/09/03/12-big-data-definitions-whats-yours/ Sharda, R., Delen, D., & Turban, E. (2014). Business Intelligence and Analytics: Systems for Decision Support. Prentice Hall. Sullivan, B., & Mitra, S. (2014, March). Community Issues in American Metropolitan Cities: A Data Mining Case Study. Retrieved January 25, 2015, from Journal of Cases on Information Technology: http://www.irma-international.org/viewtitle/109515/ Read More